Gamut Constrained Illuminant Estimation

Gamut Constrained Illuminant Estimation G. D. Finlayson and S. D. Hordley School of Computing Sciences, University of East Anglia, UK I. Tastl Hewlett Packard Incorporated, Palo Alto, USA Abstract. This paper presents a novel solution to the illuminant estimation problem: the problem of how, given an image of a scene taken under an unknown illuminant, we can recover an estimate of that light. The work is founded on previous gamut mapping solutions to the problem which solve for a scene illuminant by determining the set of diagonal mappings which take image data captured under an unknown light to a gamut of reference colours taken under a known light. Unfortunately, a diagonal model is not always a valid model of illumination change and so previous approaches sometimes return a null solution. In addition, previous methods are difficult to implement. We address these problems by recasting the problem as one of illuminant classification: we define a priori a set of plausible lights thus ensuring that a scene illuminant estimate will always be found. A plausible light is represented by the gamut of colours observable under it and the illuminant in an image is classified by determining the plausible light whose gamut is most consistent with the image data. We show that this step (the main computational burden of the algorithm) can be performed simply and efficiently by means of a non-negative least-squares optimisation. We report results on a large set of real images which show that it provides excellent illuminant estimation, outperforming previous algorithms. Keywords: Colour Constancy, Illuminant Estimation, Gamut Mapping 1. Introduction It is well established that colour is a useful cue which helps to solve a number of classical computer vision problems such as object recog- nition [29], object tracking [27] and image segmentation [8]. However, implicit in this use of colour is the assumption that colour is an inherent property of an object. In reality an object’s colour depends in equal measure on the object’s physical properties and the characteristics of the light by which it is illuminated. That is, the image colours recorded by a camera change when the colour of the light illuminating the scene is changed. One way to deal with this problem is to derive so called colour invariant features from the image data which are invariant to the prevailing illumination (for some examples of such an approach see [16, 15, 17]). An alternative approach, which is the focus of this paper, is to correct images to account for the effect of the scene illuminant on recorded colours. This correction procedure is typically c 2006 Kluwer Academic Publishers. Printed in the Netherlands. FinHorTastl_IJCV05.tex; 27/02/2006; 20:35; p.1 2 achieved in a two-stage process. In the first stage the colour of the scene illumination is estimated from the image data. Then, the estimate of the scene illuminant is used to correct the recorded image such that the corrected image is independent of the scene illuminant. The first stage of the process – estimating the scene illuminant – is the more difficult of the two and it is this problem which is the subject of this paper. Despite much research [22, 6, 24, 14, 9, 7, 10, 2, 3] there does not exist a satisfactory solution to the illuminant estimation problem. Early attempts at a solution sought to simplify the problem by making certain assumptions about the composition of a scene. For example it was assumed that a scene will contain a “white” (maximally reflective) surface [22] or that the average of all surfaces in the scene is neutral [6]. Other authors modelled lights and surfaces by low-dimensional linear models and derived algebraic solutions to the problem [24]. It is easy to understand why such approaches do not work in practice: the constraints they place on scenes are too strong. More recently a number of more sophisticated algorithms have been developed [14, 7, 10, 5, 28] and these approaches can often give reasonable illuminant estimation. These methods work not by placing constraints on scenes but by exploiting prior knowledge about the nature of objects and surfaces. For example, the Neural Network (NN) approach of Funt et al [7] attempts to learn the relationship between image data and scene illuminant on the basis of a large number of example images. While it performs reasonably well this approach is unsatisfactory because it provides a black box solution to the problem which gives little insight into the problem itself. In addition neural networks are non-trivial to implement and they rely for their success on training data which properly reflects the statistics of the world. In practice neural networks often do not generalise well and this has been found to be the case for illuminant estimation [3]. Perhaps, a more promising approach is the Correlation Matrix (CM) algorithm of Finlayson et al [10] which has the advantage of being simple both in its implementation and in terms of the computations which must be carried out to estimate the illuminant. This method gains part of its simplicity from the observation that the set of possible scene lights is quite restricted and can be adequately represented by considering just a small discrete set of plausible lights. The method works by exploiting the fact that the statistical distribution of possible image colours varies as the scene illuminant changes. Using this fact, and given a set of image data, it is possible to obtain a measure of the probability that each of the set of plausible illuminants was the scene light. Clearly, the approach relies on having a reasonable statistical model of lights and surfaces: (i.e. it requires accurate training data to FinHorTastl_IJCV05.tex; 27/02/2006; 20:35; p.2 3 work in practice) but unfortunately such data is difficult to obtain. A second disadvantage of the method is that it works not with 3-d RGB camera data but with 2-d chromaticity data: i.e. brightness information is discarded. There is evidence to suggest however [4, 30] that brightness information is important in estimating the scene illuminant and while brightness information can be incorporated into the correlation matrix algorithm [4], doing so destroys the simplicity of the approach. A third approach which has also shown promise is the Gamut Map- ping (GM) method first proposed by Forsyth [14]. Gamut Mapping also exploits knowledge about the properties of surfaces and lights to estimate the scene illuminant. However, it is not based on a statistical analysis of the frequency with which surfaces occur in the world but on the simple observation that the set of camera RGBs observable under a given illuminant is a bounded convex set. This observation follows directly from physical constraints on the nature of a surface (all surfaces reflect between 0 and 100% of light incident upon them) and the linear nature of image formation. It therefore exploits the minimal assumptions that can be made about the world. Forsyth exploited this observation by characterising the gamut of possible image colours which can be observed under a reference light. He called this set the canonical gamut. The colours in an image whose scene illuminant it is wished to estimate can also be characterised as a set: the image gamut. Estimating the scene illuminant can then be cast as the problem of finding the mapping which takes the image gamut into the canonical gamut. To properly define the algorithm requires that the form of the mapping between the two sets be specified. That is, the relationship between sensor responses under two different illuminants must be defined. Forsyth modelled illumination change using a diagonal model [14] under which sensor responses for a surface viewed under two different lights are related by simple scale factors which depend on the sensors and the pair of lights but which are independent of the surface. Using this diagonal model of illumination change Forsyth derived the CRULE algorithm which estimates the scene illuminant in two stages. First the set of all mappings (the feasible set) which take the image gamut to the canonical gamut are determined: usually many mappings are consistent with the image data. In the second stage a single mapping is selected from this feasible set according to some pre-defined selection criterion. The algorithm has been found to perform quite well [3] but it suffers from a number of limitations. It is these limitations which we aim to address in this paper. We do this by proposing a new algorithm which shares some of the fundamental ideas of the original work but which is implemented in such a way as to avoid its weaknesses. FinHorTastl_IJCV05.tex; 27/02/2006; 20:35; p.3 4 The first weakness of CRULE is the fact that the algorithm is founded on the assumption that illumination change can be well modelled by a diagonal transform. This leads to an algorithm which is concep- tually simple but which can nevertheless fail. One reason for this is that real images can contain an illuminant or (more realistically) one or more image colours which do not conform to the diagonal model of illumination change and unfortunately in such situations the theoretical foundation of the algorithm implies that there is no illuminant estimate which satisfies all the image data. That is, there does not exist a diagonal transform that maps the image gamut inside the canonical gamut. While this problem might be ameliorated by carrying out a change of sensor basis (and using a generalised diagonal transform [11]) it does not completely disappear: a linear model of illumination change is only approximate.

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